%0 Conference Proceedings %T SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues %A Qiu, Liang %A Liang, Yuan %A Zhao, Yizhou %A Lu, Pan %A Peng, Baolin %A Yu, Zhou %A Wu, Ying Nian %A Zhu, Song-Chun %Y Zong, Chengqing %Y Xia, Fei %Y Li, Wenjie %Y Navigli, Roberto %S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F qiu-etal-2021-socaog %X Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an α-β-γ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an α process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a β process updating the social relations based on related attributes, and (iii) a γ process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference. %R 10.18653/v1/2021.acl-long.54 %U https://aclanthology.org/2021.acl-long.54 %U https://doi.org/10.18653/v1/2021.acl-long.54 %P 658-670